


import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE     :			Tuple        =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :			Optional[int]        =							{
    """b0""": efficientnet.EfficientNetBa,
    """b1""": efficientnet.EfficientNetBa,
    """b2""": efficientnet.EfficientNetBa,
    """b3""": efficientnet.EfficientNetBa,
    """b4""": efficientnet.EfficientNetBa,
    """b5""": efficientnet.EfficientNetBa,
    """b6""": efficientnet.EfficientNetBa,
    """b7""": efficientnet.EfficientNetBa,
}

_SCREAMING_SNAKE_CASE     :			Dict        =							{
    """b0""": {
        """hidden_dim""": 1_280,
        """width_coef""": 1.0,
        """depth_coef""": 1.0,
        """image_size""": 224,
        """dropout_rate""": 0.2,
        """dw_padding""": [],
    },
    """b1""": {
        """hidden_dim""": 1_280,
        """width_coef""": 1.0,
        """depth_coef""": 1.1,
        """image_size""": 240,
        """dropout_rate""": 0.2,
        """dw_padding""": [16],
    },
    """b2""": {
        """hidden_dim""": 1_408,
        """width_coef""": 1.1,
        """depth_coef""": 1.2,
        """image_size""": 260,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 8, 16],
    },
    """b3""": {
        """hidden_dim""": 1_536,
        """width_coef""": 1.2,
        """depth_coef""": 1.4,
        """image_size""": 300,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 18],
    },
    """b4""": {
        """hidden_dim""": 1_792,
        """width_coef""": 1.4,
        """depth_coef""": 1.8,
        """image_size""": 380,
        """dropout_rate""": 0.4,
        """dw_padding""": [6],
    },
    """b5""": {
        """hidden_dim""": 2_048,
        """width_coef""": 1.6,
        """depth_coef""": 2.2,
        """image_size""": 456,
        """dropout_rate""": 0.4,
        """dw_padding""": [13, 27],
    },
    """b6""": {
        """hidden_dim""": 2_304,
        """width_coef""": 1.8,
        """depth_coef""": 2.6,
        """image_size""": 528,
        """dropout_rate""": 0.5,
        """dw_padding""": [31],
    },
    """b7""": {
        """hidden_dim""": 2_560,
        """width_coef""": 2.0,
        """depth_coef""": 3.1,
        """image_size""": 600,
        """dropout_rate""": 0.5,
        """dw_padding""": [18],
    },
}


def 							__lowerCAmelCase      (			__magic_name__      ):
 _lowercase:       List[Any]						=				EfficientNetConfig()
 _lowercase:       Tuple						=				CONFIG_MAP[model_name]["hidden_dim"]
 _lowercase:       List[Any]						=				CONFIG_MAP[model_name]["width_coef"]
 _lowercase:       int						=				CONFIG_MAP[model_name]["depth_coef"]
 _lowercase:       List[Any]						=				CONFIG_MAP[model_name]["image_size"]
 _lowercase:       List[str]						=				CONFIG_MAP[model_name]["dropout_rate"]
 _lowercase:       Optional[int]						=				CONFIG_MAP[model_name]["dw_padding"]

 _lowercase:       Optional[Any]						=				"huggingface/label-files"
 _lowercase:       List[Any]						=				"imagenet-1k-id2label.json"
 _lowercase:       str						=				1_0_0_0
 _lowercase:       Dict						=				json.load(open(hf_hub_download(__magic_name__ ,   __magic_name__ ,   repo_type="dataset"      ) ,   "r"      )      )
 _lowercase:       Any						=				{int(__magic_name__      ): v for k, v in idalabel.items()}

 _lowercase:       Optional[int]						=				idalabel
 _lowercase:       int						=				{v: k for k, v in idalabel.items()}
 return config


def 							__lowerCAmelCase      (			):
 _lowercase:       Union[str, Any]						=				"http://images.cocodataset.org/val2017/000000039769.jpg"
 _lowercase:       Dict						=				Image.open(requests.get(__magic_name__ ,   stream=__magic_name__      ).raw      )
 return im


def 							__lowerCAmelCase      (			__magic_name__      ):
 _lowercase:       int						=				CONFIG_MAP[model_name]["image_size"]
 _lowercase:       Tuple						=				EfficientNetImageProcessor(
     size={"height": size, "width": size} ,   image_mean=[0.485, 0.456, 0.406] ,   image_std=[0.47_853_944, 0.4_732_864, 0.47_434_163] ,   do_center_crop=__magic_name__ ,   )
 return preprocessor


def 							__lowerCAmelCase      (			__magic_name__      ):
 _lowercase:       List[str]						=				[v.split("_"      )[0].split("block"      )[1] for v in original_param_names if v.startswith("block"      )]
 _lowercase:       str						=				sorted(set(__magic_name__      )      )
 _lowercase:       Optional[int]						=				len(__magic_name__      )
 _lowercase:       Union[str, Any]						=				{b: str(__magic_name__      ) for b, i in zip(__magic_name__ ,   range(__magic_name__      )      )}

 _lowercase:       Any						=				[]
 rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight")      )
 rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight")      )
 rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias")      )
 rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean")      )
 rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var")      )

 for b in block_names:
  _lowercase:       List[Any]						=				block_name_mapping[b]
  rename_keys.append((f"block{b}_expand_conv/kernel:0", f"encoder.blocks.{hf_b}.expansion.expand_conv.weight")      )
  rename_keys.append((f"block{b}_expand_bn/gamma:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.weight")      )
  rename_keys.append((f"block{b}_expand_bn/beta:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.bias")      )
  rename_keys.append(
      (f"block{b}_expand_bn/moving_mean:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean")      )
  rename_keys.append(
      (f"block{b}_expand_bn/moving_variance:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_var")      )
  rename_keys.append(
      (f"block{b}_dwconv/depthwise_kernel:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight")      )
  rename_keys.append((f"block{b}_bn/gamma:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight")      )
  rename_keys.append((f"block{b}_bn/beta:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias")      )
  rename_keys.append(
      (f"block{b}_bn/moving_mean:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean")      )
  rename_keys.append(
      (f"block{b}_bn/moving_variance:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var")      )

  rename_keys.append((f"block{b}_se_reduce/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight")      )
  rename_keys.append((f"block{b}_se_reduce/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias")      )
  rename_keys.append((f"block{b}_se_expand/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.weight")      )
  rename_keys.append((f"block{b}_se_expand/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.bias")      )
  rename_keys.append(
      (f"block{b}_project_conv/kernel:0", f"encoder.blocks.{hf_b}.projection.project_conv.weight")      )
  rename_keys.append((f"block{b}_project_bn/gamma:0", f"encoder.blocks.{hf_b}.projection.project_bn.weight")      )
  rename_keys.append((f"block{b}_project_bn/beta:0", f"encoder.blocks.{hf_b}.projection.project_bn.bias")      )
  rename_keys.append(
      (f"block{b}_project_bn/moving_mean:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_mean")      )
  rename_keys.append(
      (f"block{b}_project_bn/moving_variance:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_var")      )

 rename_keys.append(("top_conv/kernel:0", "encoder.top_conv.weight")      )
 rename_keys.append(("top_bn/gamma:0", "encoder.top_bn.weight")      )
 rename_keys.append(("top_bn/beta:0", "encoder.top_bn.bias")      )
 rename_keys.append(("top_bn/moving_mean:0", "encoder.top_bn.running_mean")      )
 rename_keys.append(("top_bn/moving_variance:0", "encoder.top_bn.running_var")      )

 _lowercase:       Dict						=				{}
 for item in rename_keys:
  if item[0] in original_param_names:
   _lowercase:       str						=				"efficientnet." + item[1]

 _lowercase:       Optional[Any]						=				"classifier.weight"
 _lowercase:       Optional[Any]						=				"classifier.bias"
 return key_mapping


def 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__ ,   __magic_name__      ):
 for key, value in tf_params.items():
  if "normalization" in key:
   continue

  _lowercase:       str						=				key_mapping[key]
  if "_conv" in key and "kernel" in key:
   _lowercase:       Union[str, Any]						=				torch.from_numpy(__magic_name__      ).permute(3 ,   2 ,   0 ,   1      )
  elif "depthwise_kernel" in key:
   _lowercase:       Optional[Any]						=				torch.from_numpy(__magic_name__      ).permute(2 ,   3 ,   0 ,   1      )
  elif "kernel" in key:
   _lowercase:       str						=				torch.from_numpy(np.transpose(__magic_name__      )      )
  else:
   _lowercase:       int						=				torch.from_numpy(__magic_name__      )

  # Replace HF parameters with original TF model parameters
  assert hf_params[hf_key].shape == new_hf_value.shape
  hf_params[hf_key].copy_(__magic_name__      )


@torch.no_grad()
def 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__ ,   __magic_name__ ,   __magic_name__      ):
 _lowercase:       List[Any]						=				model_classes[model_name](
     include_top=__magic_name__ ,   weights="imagenet" ,   input_tensor=__magic_name__ ,   input_shape=__magic_name__ ,   pooling=__magic_name__ ,   classes=1_0_0_0 ,   classifier_activation="softmax" ,   )

 _lowercase:       List[Any]						=				original_model.trainable_variables
 _lowercase:       int						=				original_model.non_trainable_variables
 _lowercase:       Tuple						=				{param.name: param.numpy() for param in tf_params}
 for param in tf_non_train_params:
  _lowercase:       Tuple						=				param.numpy()
 _lowercase:       Optional[int]						=				list(tf_params.keys()      )

 # Load HuggingFace model
 _lowercase:       Optional[Any]						=				get_efficientnet_config(__magic_name__      )
 _lowercase:       int						=				EfficientNetForImageClassification(__magic_name__      ).eval()
 _lowercase:       Dict						=				hf_model.state_dict()

 # Create src-to-dst parameter name mapping dictionary
 print("Converting parameters..."      )
 _lowercase:       Tuple						=				rename_keys(__magic_name__      )
 replace_params(__magic_name__ ,   __magic_name__ ,   __magic_name__      )

 # Initialize preprocessor and preprocess input image
 _lowercase:       Union[str, Any]						=				convert_image_processor(__magic_name__      )
 _lowercase:       str						=				preprocessor(images=prepare_img() ,   return_tensors="pt"      )

 # HF model inference
 hf_model.eval()
 with torch.no_grad():
  _lowercase:       Tuple						=				hf_model(**__magic_name__      )
 _lowercase:       Union[str, Any]						=				outputs.logits.detach().numpy()

 # Original model inference
 _lowercase:       str						=				False
 _lowercase:       List[Any]						=				CONFIG_MAP[model_name]["image_size"]
 _lowercase:       str						=				prepare_img().resize((image_size, image_size) ,   resample=PIL.Image.NEAREST      )
 _lowercase:       List[Any]						=				image.img_to_array(__magic_name__      )
 _lowercase:       Any						=				np.expand_dims(__magic_name__ ,   axis=0      )
 _lowercase:       Optional[int]						=				original_model.predict(__magic_name__      )

 # Check whether original and HF model outputs match  -> np.allclose
 assert np.allclose(__magic_name__ ,   __magic_name__ ,   atol=1e-3      ), "The predicted logits are not the same."
 print("Model outputs match!"      )

 if save_model:
  # Create folder to save model
  if not os.path.isdir(__magic_name__      ):
   os.mkdir(__magic_name__      )
  # Save converted model and image processor
  hf_model.save_pretrained(__magic_name__      )
  preprocessor.save_pretrained(__magic_name__      )

 if push_to_hub:
  # Push model and image processor to hub
  print(f"Pushing converted {model_name} to the hub..."      )
  _lowercase:       Union[str, Any]						=				f"efficientnet-{model_name}"
  preprocessor.push_to_hub(__magic_name__      )
  hf_model.push_to_hub(__magic_name__      )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE     :			int        =							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--model_name',
           default='b0',
           type=str,
           help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path',
           default='hf_model',
           type=str,
           help='Path to the output PyTorch model directory.',
       )
       parser.add_argument('--save_model', action='store_true', help='Save model to local')
       parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')

       _SCREAMING_SNAKE_CASE     :			Optional[int]        =							parser.parse_args()
       convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)


"""simple docstring"""



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE__:str									=			HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__:List[str]									=			parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE__:List[Any]									=			AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			{
    """vocab_size""": len(tokenizer),
    """scale_attn_by_inverse_layer_idx""": True,
    """reorder_and_upcast_attn""": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE__:Optional[int]									=			AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE__:Tuple									=			AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
